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<title>Computes the fastest distributed linear averaging (FDLA) edge weights</title>
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<h1>Computes the fastest distributed linear averaging (FDLA) edge weights</h1>
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<span class="keyword">function</span> [ w, cvx_optval ] = fdla( A )

<span class="comment">% [W,S] = FDLA(A) gives a vector of the fastest distributed linear averaging</span>
<span class="comment">% edge weights for a graph described by the incidence matrix A (n x m).</span>
<span class="comment">% Here n is the number of nodes and m is the number of edges in the graph;</span>
<span class="comment">% each column of A has exactly one +1 and one -1.</span>
<span class="comment">%</span>
<span class="comment">% The FDLA edge weights are given by the SDP:</span>
<span class="comment">%</span>
<span class="comment">%   minimize    s</span>
<span class="comment">%   subject to  -s*I &lt;= I - L - (1/n)11' &lt;= s*I</span>
<span class="comment">%</span>
<span class="comment">% where the variables are edge weights w in R^m and s in R.</span>
<span class="comment">% Here L is the weighted Laplacian defined by L = A*diag(w)*A'.</span>
<span class="comment">% The optimal value is s, and is returned in the second output.</span>
<span class="comment">%</span>
<span class="comment">% For more details see the references:</span>
<span class="comment">% "Fast linear iterations for distributed averaging" by L. Xiao and S. Boyd</span>
<span class="comment">% "Convex Optimization of Graph Laplacian Eigenvalues" by S. Boyd</span>
<span class="comment">%</span>
<span class="comment">% Written for CVX by Almir Mutapcic 08/29/06</span>

[n,m] = size(A);
I = eye(n,n);
J = I - (1/n) * ones(n,n);
cvx_begin <span class="string">sdp</span>
    variable <span class="string">w(m,1)</span>   <span class="comment">% edge weights</span>
    variable <span class="string">s</span>        <span class="comment">% epigraph variable</span>
    variable <span class="string">L(n,n)</span> <span class="string">symmetric</span>
    minimize( s )
    subject <span class="string">to</span>
        L == A * diag(w) * A';
        -s * I &lt;= J - L &lt;= s * I;
cvx_end
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